Data quality management as a factor in increasing the efficiency of enterprise project activities

Background. In modern enterprise project management, data is a key resource, determining the validity of management decisions and the effectiveness of project implementation. The expansion of digital processes is accompanied by an increase in the volume of information and the complexity of information flows, increasing the risk of inaccuracies, inconsistencies, and the use of outdated information. Such distortions impact timeline and cost calculations, departmental coordination, and project risk management, reducing the sustainability of project management. Purpose. Development of a scientifically based solution for managing data quality in the enterprise’s project activities, which will improve the accuracy of planning, the sustainability of management decisions, and the effectiveness of project results. Methodology. The study relies on empirical and theoretical analysis to examine existing approaches to data quality management in project activities. To achieve this goal, comparative analysis and logical systematization methods were used to identify key data quality criteria (accuracy, completeness, consistency, relevance, and structure) and their impact on project performance. Content analysis was also used to examine regulatory documents and standards governing data management, and a case study approach was used to analyze real‑world examples from companies with digital infrastructure. Results. A literature review identified key data quality criteria relevant to project activities: accuracy, completeness, consistency, relevance, and structure. These criteria were systematized and compared with project cycle stages, allowing us to determine at which stages data quality violations have the greatest impact on timelines, budgets, and risk management. A comparison of various approaches presented in publications and regulatory documents revealed that most methodologies focus on the technical aspects of working with data and do not consider the specifics of the project environment. Based on this, requirements were formulated for an approach that combines management and information elements and can be integrated into existing enterprise processes. A company case study confirmed the significance of the identified criteria. An analysis of working documentation and examples of deviations demonstrated that data errors primarily arise at the intersection of departments, and their consequences affect procurement planning, labor cost calculation, and task execution monitoring. Interpretation of the case data revealed several typical situations in which data inconsistencies lead to time and resource overruns. The obtained results form the basis for developing an approach to data quality management focused on the requirements of project activities and the real practical conditions of the enterprise. Practical implications. The research findings can be used in various industries and organizations involved in project‑based activities to develop and implement effective data quality management systems. The proposed approach will help improve planning accuracy, reduce risks associated with insufficient or outdated information, and improve project management in the digital economy.